Project Summary/Abstract Over the past decade, serial-section electron microscopy has come into its own as a method to study the connectivity of neural circuits, from local circuits in mammals to entire invertebrate brains. Recently, the emphasis in the field has been to create increasingly large data sets, while comparatively little effort has been spent on making the tools of EM connectomics available to a large number of circuit neuroscientists. Obstacles exist at multiple levels. Manual approaches to serial sectioning are prohibitively difficult, while automated approaches require complex, expensive equipment that is difficult to deploy. High throughput scanning EM is limited to multi- beam approaches that are extremely expensive. Transmission EM is far less expensive, but automated approaches to sectioning remain challenging and require expensive substrates that are hard to manufacture and difficult to use. We propose to develop a new approach, already prototyped by our group and our industry partner, to establish a robust platform optimized to achieve the widest possible adoption. The system will center on an open source serial sectioning robot implementing a novel collection approach. The goal is to create a system that can be used at a variety of scales, from the current state of the art (1 mm3 or greater), to small volumes that can be sectioned and imaged routinely. Up to now, each published EM volume for connectomics has required a multi-year effort. Instead, our goal is to use volume reconstruction as an assay, rather than an end unto itself, in the context of other experiments. In the final year, we will create data sets that test the flexibility and robustness of the approach by creating EM volumes ranging from 50µm on a side to very large volumes encompassing >1 mm3.